Some physical reservoirs are promising for developing next-generation machine learning hardware devices and chips. Such hardware-based RC can significantly speed up data processing compared to software-based RC, and it is often motivated by the need to reduce the power consumed by machine learning hardware. In particular, the RC framework using a simple readout is suited to low-cost real-time computation, which is not achievable with other machine learning frameworks based on iterative learning algorithms leading to high learning cost. Currently, many online services rely on cloud computing [1]} where tasks are performed at computers far from devices of end users on the Internet. Owing to the ever-growing amount of data at network edges, increasing communication latency is becoming a bottleneck for high-speed cloud computing. An emerging alternative computing paradigm for reducing the latency is edge computing [2]} where tasks are performed at computers close to devices of end users and sensors, preferably in real time. Machine learning hardware for real-time temporal data processing, such as RC hardware, enables efficient edge computing, and therefore, is expected to become increasingly significant in the future. On the other hand, physical constraints make it difficult to optimize the conditions of reservoirs in physical RC. Many issues remain to be addressed in order to realize efficient physical RC systems and devices for practical applications. Our review introduces potential candidates of physical reservoirs for such devices.
